Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach

Micanaldo Ernesto Francisco; Thaddeus M Carvajal; Masahiro Ryo; Kei Nukazawa; Divina M Amalin; Kozo Watanabe

doi:10.1016/j.scitotenv.2021.148406

Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach

Sci Total Environ. 2021 Oct 20:792:148406. doi: 10.1016/j.scitotenv.2021.148406. Epub 2021 Jun 10.

Authors

Micanaldo Ernesto Francisco¹, Thaddeus M Carvajal², Masahiro Ryo³, Kei Nukazawa⁴, Divina M Amalin⁵, Kozo Watanabe⁶

Affiliations

¹ Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama 790-8577, Japan; Graduate School of Science and Engineering, Ehime University, Matsuyama 790-8577, Japan.
² Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama 790-8577, Japan; Graduate School of Science and Engineering, Ehime University, Matsuyama 790-8577, Japan; Biology Department, De La Salle University, Taft Ave, Manila 1004, Philippines; Biological Control Research Unit, Center for Natural Science and Environmental Research, De La Salle University, Taft Ave, Manila, Philippines.
³ Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany; Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, Germany.
⁴ Department of Civil and Environmental Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.
⁵ Biology Department, De La Salle University, Taft Ave, Manila 1004, Philippines; Biological Control Research Unit, Center for Natural Science and Environmental Research, De La Salle University, Taft Ave, Manila, Philippines.
⁶ Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama 790-8577, Japan; Graduate School of Science and Engineering, Ehime University, Matsuyama 790-8577, Japan; Biology Department, De La Salle University, Taft Ave, Manila 1004, Philippines; Biological Control Research Unit, Center for Natural Science and Environmental Research, De La Salle University, Taft Ave, Manila, Philippines. Electronic address: watanabe.kozo.mj@ehime-u.ac.jp.

PMID: 34157535
DOI: 10.1016/j.scitotenv.2021.148406

Abstract

Background: Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints.

Objectives: To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence.

Methods: Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence.

Results: The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas.

Conclusions: Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.

Keywords: Combined influences; Dengue; Environmental factors; Random forest; Recursive partitioning.

MeSH terms

Animals
Culicidae*
Dengue* / epidemiology
Machine Learning
Mosquito Vectors
Philippines